The Use of Artificial Intelligence in Aviation: A Bibliometric Analysis
DOI:
https://doi.org/10.22399/ijcesen.747Keywords:
Artificial Intelligence in Aviation, Machine Learning, deep learning, BibliometricsAbstract
The bibliometric analysis of 395 articles selected from the Web of Science (WoS) database between 2004 and 2024 is designed to provide a foundation for future research by mapping scientific collaborations, conceptual clusters, citation relationships, and intellectual structures in the research area, highlighting the international scope of the research area, and identifying emerging trends and influential studies. The results show that dominant topics such as machine learning, deep learning, aviation safety, atmospheric modeling, and anomaly detection are being studied in academia, highlighting the central role of AI in improving aviation safety and operational efficiency. High-impact journals such asIEEE Access and Aerospace have emerged as leading platforms, while Transportation Research Part C and the Journal of Air Transport Management are prominent in logistics and aviation-focused research. China and the United States lead aerospace and AI research with high publication volumes and significant impact. Italy contributes fewer publications but makes a notable impact, while the United Kingdom plays an important role in this field with active research efforts. Institutions such as Nanjing University of Aeronautics, Astronautics, and Vanderbilt University play an important role in advancing the field. These data show that, on both a journal and country basis, certain centers and countries have assumed dominant roles in the global research agenda in aerospace and AI, which have directly contributed to the formation of the aerospace ecosystem. These results provide important clues as to where future research will focus, and show that research communities are increasingly collaborating.
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